Agenda

RStudio

Setup:
- How I do Python install and package management
- Setting up RStudio for Python use

Agenda

reticulate
- Environments
- Interacting with Python from R
- Type conversions
Live demo?

About Me

Who is Jennifer Knack?

Quick aside: RStudio

Disclaimer

R is my preferred coding language. All code in this talk, unless specified, is R code.

I use RStudio as my preferred IDE, and this talk was written from that perspective.

You can just as easily do this all in VSCode. But please…

Don’t use Jupyter Notebooks.

Hidden states hinder reproduciblity
- Cells can be run in any order, any number of times. According to one study¹, 73% of all Jupyter notebooks are not reproducible with straightforward approaches.
Version control is nearly impossible because Jupyter notebooks are stored as single-line JSON files
- Merging branches with git versioning nearly always breaks because git version control works on a line-by-line basis
- Visualizing and understanding differences between versions is an impossible task
No linting, no style help, no IDE integration
Test-driven development is very difficult
Jupyter notebooks don’t scale well with big data

RStudio is objectively the best IDE

easy GUI with console and terminal windows
easy, integrated package management (for R)
easy, integrated report/presentation generation via Rmarkdown or Quarto
git integration
easily view environment objects and run history
linting, syntax help, and tab-completion all built in
easily navigate and view files and set working directory
plot view and help windows

“But why not just use VSCode?”

Because I don’t want to.

I like (and more importantly, know) RStudio.

Setup

Python install and package management

There are utilities to install and manage Python packages and environments in RStudio² but I don’t use them.

I use mamba³ instead.

Setting up RStudio to run Python

Tell RStudio where you installed Python

Go to Tools -> Global Options

Setting up RStudio to run Python

Enter the path to the python interpreter in your base environment

Setting up RStudio to run Python

Install the reticulate package

install.packages("reticulate")

reticulate⁴ is a Tidyverse package containing tools for interoperability between Python and R.

reticulate

What does reticulate do?⁵

Allows calling Python from R in multiple ways including from RMarkdown/Quarto, sourcing Python scripts, importing Python modules, and using Python interactively within an R session

Translates between R and Python objects (e.g., between R and Pandas data frames, or between R matrices and NumPy arrays)

Provides flexible binding to different versions of Python including virtual environments and Conda environments

A note from Posit about the philosophy behind Python tools in Rstudio

These tools are not intended for standalone Python work but rather explicitly aimed at the integration of Python into R projects (and as such are closely tied to the reticulate package).

They “strongly suggest” using one of the IDEs available for doing data science in Python for Python-only projects.⁶

What does reticulate do?

Allows calling Python from R in multiple ways including from RMarkdown/Quarto, sourcing Python scripts, importing Python modules, and using Python interactively within an R session
Translates between R and Python objects (e.g., between R and Pandas data frames, or between R matrices and NumPy arrays)

Provides flexible binding to different versions of Python including virtual environments and Conda environments

Set your environment

First thing you always want to do is set your environment:

library(reticulate)
use_condaenv("iffrug")

Do this whether you’re working interactively, calling scripts, or authoring reports.

There is also use_python() that allows you to specify an alternative version of python other than the one you set in your global options, or use_virtualenv() to set a virtual environment instead of a conda environment.

What does reticulate do?

Allows calling Python from R in multiple ways including from RMarkdown/Quarto, sourcing Python scripts, importing Python modules, and using Python interactively within an R session

Translates between R and Python objects (e.g., between R and Pandas data frames, or between R matrices and NumPy arrays)
Provides flexible binding to different versions of Python including virtual environments and Conda environments

The `py` object

When you call library(reticulate), it creates the py object in the reticulate package environment.

It is the bridge between R and Python, through which you can run Python code and interact with Python objects.

The most common way you will interact with it is to access any Python object from the R environment using the $ operator, e.g., py$x.

Important

Always call library(reticulate) or you won’t be able to access the py object!

The `r` object

Similarly, reticulate creates the r object in the Python environment it creates. Through it, you can access R objects in the Python environment.

You can access R objects using the . operator, e.g., r.x.

Note

Examples of using the py and r objects are found further in this presentation

Importing modules

reticulate::import() can be used to import any installed Python module into your R environment.

os <- import("os")

Then you can call any function from that module in R using $.

os$listdir(".") |> head()

[1] ".git"                                ".gitignore"                         
[3] ".Rbuildignore"                       ".Rhistory"                          
[5] ".Rproj.user"                         "2024-04-24_python-in-rmarkdown.html"

If you’d like to access built in Python functions, use import_builtins().

builtins <- import_builtins()
builtins$print('Hello, World!')

Hello, World!

Sourcing scripts

Let’s say I have a Python script that defines a function:

## this is Python code

def add(x,y):
  return x + y

If I’d like to use that function in R, I can source it using source_python().

source_python('add.py')
add(5,10)

[1] 15

Executing code

Let’s say my collaborator wrote a Python script for processing some raw data. I’d like to work with the processed data in R, but my collaborator only provided me with the raw data and the script.

I know the script requires a variable file that is a character string pointing to the path of the raw data, and outputs a Pandas data frame called df.

I can use py_run_string() and py_run_file() to process the data, and then access any objects created into the Python main module using the py object exported by reticulate:

# Set the Python variable pointing to the raw data file
py_run_string("file = 'extdata/rawdata.csv'")

# run the processing script, which takes the file argument
py_run_file("process_raw_data.py")

# access the resulting df
py$df

Working in RMarkdown⁷

reticulate includes a Python engine for RMarkdown, and knitr v 1.18 and higher uses this engine by default.

Set your environment in your setup chunk:

Important

Always call library(reticulate) or you won’t be able to access the py object!

Then you can start inserting Python chunks just like you would R chunks, and knitr will knit everything together:

Just like when working interactively, you can access objects created in Python chunks in R by using the py object:

And you can access objects created in R chunks in Python by using the r object:

Working in Quarto

Quarto provides all the support for Python that RMarkdown does, plus support for Jupyter:

Quarto supports rendering with the Jupyter kernel in addition to knitr and reticulate – just put jupyter: python3 in your YAML header and make sure the paths to Python and Jupyter are in your PATH.

You can also provide a full kernelspec in your YAML:

---
title: "My Document"
jupyter:
  kernelspec:
    name: xpython
    language: "python"
    display_name: "Python 3.7 (XPython)"
---

Because it can use the Jupyter kernel, Quarto CLI can render Jupyter notebooks too:

quarto render document.ipynb

There is other support for Python from the Quarto CLI as well, plus a VSCode Quarto plugin.⁸

Working with Python interactively

If you want to work with Python interactively, you can call repl_python() to initiate a Python REPL embedded in the R console.

You can use the py and r objects to access objects between environments.

What does reticulate do?

Allows calling Python from R in multiple ways including from RMarkdown/Quarto, sourcing Python scripts, importing Python modules, and using Python interactively within an R session

Translates between R and Python objects (e.g., between R and Pandas data frames, or between R matrices and NumPy arrays)

Provides flexible binding to different versions of Python including virtual environments and Conda environments

Type conversions

When calling into Python, R data types are automatically converted to their equivalent Python types.

When values are returned from Python to R they are converted back to R types.⁹

The automatic conversion between R types and Python types works well in most cases, but sometimes you might want more control over the conversions.

Conversion table

R	Python	Examples
Single-element vector	Scalar	`1`, `1L`, `TRUE`, `"foo"`
Multi-element vector	List	`c(1.0, 2.0, 3.0)`, `c(1L, 2L, 3L)`
List of multiple types	Tuple	`list(1L, TRUE, "foo")`
Named list	Dict	`list(a = 1L, b = 2.0)`, `dict(x = x_data)`
Matrix/Array	NumPy ndarray	`matrix(c(1,2,3,4), nrow = 2, ncol = 2)`
Data Frame	Pandas Dataframe	`data.frame(x = c(1,2,3), y = c("a", "b", "c"))`
Function	Python function	`function(x) x + 1`
Raw	Python bytearray	`as.raw(c(1:10))`
NULL, TRUE, FALSE	None, True, False	`NULL`, `TRUE`, `FALSE`

Controling when conversion happens

If you’d like to work directly with Python objects by default you can pass convert = FALSE to the import() function.

# import numpy and specify no automatic Python to R conversion
np <- import("numpy", convert = FALSE)

# do some array manipulations with NumPy
a <- np$array(c(1:4))
(sum_np <- a$cumsum())

array([ 1,  3,  6, 10])

# what is sum?
class(sum_np)

[1] "numpy.ndarray"         "python.builtin.object"

Then when you’re done working with the object in Python, you can convert it to an R object explicitly with py_to_r().

# convert to R explicitly at the end and print object
(sum_r <- py_to_r(sum_np))

[1]  1  3  6 10

# what is sum_r?
class(sum_r)

[1] "array"

Note

We’ll be using both the sum_np and sum_r objects in examples later on, so take note of their difference

Defining the conversion

Numeric types are different between R and Python. For example, 42 in R is a float, while in Python it’s an integer.

If you want to explicitly define a number as an integer in R so that it’s passed as such to Python, use the L suffix:

class(42)

[1] "numeric"

class(42L)

[1] "integer"

If a Python API requires a list but you’re only passing it a single element, you can wrap it in base list():

foo$bar(indexes = list(42L))

Similarly, if the Python API wants a tuple, you can use reticulate::tuple():

tuple("a", "b", "c")

And if the Python API wants a dictionary, you can use reticulate::dict():

dict(foo = "bar", index = 42L)

Indices

Python uses 0-based indices for collections:

sum_np[0L]

while R uses 1-based indices:

sum_r[1]

[1] 1

Note

Notice the need to explicitly use an integer when slicing the Python object

Python indices are non-inclusive for the end range, while R indices are:

sum_np[2L:4L]

array([ 6, 10])

sum_r[2:4]

[1]  3  6 10

And negative indexing in Python counts from the end of the container, while in R it removes that index:

sum_np[-1L]

sum_r[-1]

[1]  3  6 10

Arrays

The TL;DR¹⁰

R and Python represent arrays and matrices in memory differently:

R only supports column-major order (FORTRAN-style)
NumPy supports both column-major and row-major (C-style) order, but defaults to row-major

The most important thing to remember about this is that

R and Python print arrays differently.

These are the exact same array:

(x <- np$arange(1, 9)$reshape(2L, 2L, 2L))

array([[[1., 2.],
        [3., 4.]],

       [[5., 6.],
        [7., 8.]]])

(y <- py_to_r(x))

, , 1

     [,1] [,2]
[1,]    1    3
[2,]    5    7

, , 2

     [,1] [,2]
[1,]    2    4
[2,]    6    8

More array considerations

Dense R arrays are presented to Python as column-major NumPy arrays (FORTRAN-style).
All NumPy arrays (column-major, row-major, or otherwise) are presented to R as column-major arrays, since that’s all R can understand.
R arrays are only copied to Python when they need to be, otherwise data are shared.
NumPy arrays are always copied when moved into R arrays. This can sometimes lead to multiple copies of any one array in memory at one time.

Remember this 2x2x2 array?

array([[[1., 2.],
        [3., 4.]],

       [[5., 6.],
        [7., 8.]]])

, , 1

     [,1] [,2]
[1,]    1    3
[2,]    5    7

, , 2

     [,1] [,2]
[1,]    2    4
[2,]    6    8

These are the exact same array, so why do they look different?

Python groups by the first index when printing, while R groups by the last index:

array([[[1., 2.],
        [3., 4.]],

       [[5., 6.],
        [7., 8.]]])

x[0L,,]

array([[1., 2.],
       [3., 4.]])

x[,,0L]

array([[1., 3.],
       [5., 7.]])

, , 1

     [,1] [,2]
[1,]    1    3
[2,]    5    7

, , 2

     [,1] [,2]
[1,]    2    4
[2,]    6    8

y[1,,]

     [,1] [,2]
[1,]    1    2
[2,]    3    4

y[,,1]

     [,1] [,2]
[1,]    1    3
[2,]    5    7

What about arrays from R to Python?

In the previous example I created an array in Python and ported it to R. What about the other way around?

(v <- array(1:24, c(4, 3, 2)))

, , 1

     [,1] [,2] [,3]
[1,]    1    5    9
[2,]    2    6   10
[3,]    3    7   11
[4,]    4    8   12

, , 2

     [,1] [,2] [,3]
[1,]   13   17   21
[2,]   14   18   22
[3,]   15   19   23
[4,]   16   20   24

(w <- np$array(v))

array([[[ 1, 13],
        [ 5, 17],
        [ 9, 21]],

       [[ 2, 14],
        [ 6, 18],
        [10, 22]],

       [[ 3, 15],
        [ 7, 19],
        [11, 23]],

       [[ 4, 16],
        [ 8, 20],
        [12, 24]]])

The NumPy array will be created using column-major ordering:

w$flags

  C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False

Remember:

F for “FORTRAN” (column-major order)
C for “C” (row-major order)

You can always create NumPy arrays in column-major order by passing the "F" flag:

np$reshape(np$arange(1, 25), c(4L, 3L, 2L))

array([[[ 1.,  2.],
        [ 3.,  4.],
        [ 5.,  6.]],

       [[ 7.,  8.],
        [ 9., 10.],
        [11., 12.]],

       [[13., 14.],
        [15., 16.],
        [17., 18.]],

       [[19., 20.],
        [21., 22.],
        [23., 24.]]])

np$reshape(np$arange(1, 25), c(4L, 3L, 2L), "F")

array([[[ 1., 13.],
        [ 5., 17.],
        [ 9., 21.]],

       [[ 2., 14.],
        [ 6., 18.],
        [10., 22.]],

       [[ 3., 15.],
        [ 7., 19.],
        [11., 23.]],

       [[ 4., 16.],
        [ 8., 20.],
        [12., 24.]]])

You can rearrange R arrays into row-major order, but it’s gross.

Reshaping arrays

The R dim() function is used to reshape arrays in R. This works by changing the dim attribute of the array, effectively re-interpreting the array indices using column-major semantics.

Remember though that NumPy uses row-major semantics by default; hence, its reshape method uses row-major semantics.

# NumPy reshape uses row-major semantics
np$reshape(np$arange(1,5), c(2L,2L))

array([[1., 2.],
       [3., 4.]])

So if you’re mixing R and Python code, you may get inconsistent results.

To overcome this, use reticulate::array_reshape() to reshape R arrays using row-major semantics.

# make an array from a vector
# of 4 elements:
u <- 1:4

# dim() uses
# column-major semantics
dim(u) <- c(2,2)
u

     [,1] [,2]
[1,]    1    3
[2,]    2    4

# array_reshape() uses
# row-major semantics
array_reshape(1:4, c(2,2))

     [,1] [,2]
[1,]    1    2
[2,]    3    4

Sparse matrices

reticulate supports the conversion of sparse matrices created by the Matrix R package to and from SciPy CSC matrices.¹¹

I tried to make an example but working out the dependencies for scipy.sparse was way too much work.

https://rstudio.github.io/reticulate/articles/python_dependencies.html may have been helpful but I didn’t care enough.

Data Frames

The important points

As mentioned earlier, R data frames can be automatically converted to and from Pandas data frames. By default, columns are converted using the same rules governing R array <=> NumPy array conversion, with a couple extensions:

R factors <=> Python categorical variables
R POSIXt times <=> NumPy array with dtype=datetime64[ns]

The important points

If the R data frame has row names, the generated Pandas data frame will be re-indexed using those row names, and vice versa.

If a Pandas data frame has a DatetimeIndex, it is converted to character vectors as R only supports character row names.

Using Pandas nullable data types

Pandas out of the box handles NAs differently than R:

(df <- data.frame(
  int = c(NA, 1:4),
  num = c(NA, rnorm(4)),
  lgl = c(NA, rep(c(TRUE, FALSE), 2)),
  string = c(NA, letters[1:4])
))

  int        num   lgl string
1  NA         NA    NA   <NA>
2   1 -1.6024311  TRUE      a
3   2  1.4022451 FALSE      b
4   3  0.8170408  TRUE      c
5   4 -0.2546062 FALSE      d

r_to_py(df)

          int       num    lgl string
0 -2147483648       NaN  False   None
1           1 -1.602431   True      a
2           2  1.402245  False      b
3           3  0.817041   True      c
4           4 -0.254606  False      d

However, Pandas has experimental support for nullable data types (represented by pd.NA), but you have to enable it first:

# tell Pandas to use NAs
options(reticulate.pandas_use_nullable_dtypes = TRUE)

r_to_py(df)

    int       num    lgl string
0  <NA>      <NA>   <NA>   <NA>
1     1 -1.602431   True      a
2     2  1.402245  False      b
3     3  0.817041   True      c
4     4 -0.254606  False      d

Final Points

For advanced Python users

For advanced Python users, there’s more documentation on

Contexts
Iterators
Functions
Creating high-level R interfaces for Python libraries

Check out https://rstudio.github.io/reticulate/articles/calling_python.html for deets.

Access to Python help from R

You can print documentation on any Python object using py_help():

py_help(os$chdir)

This will open a text document outside of RStudio:

Help on built-in function chdir in module nt:

chdir(path)
    Change the current working directory to the specified path.

    path may always be specified as a string.
    On some platforms, path may also be specified as an open file descriptor.
      If this functionality is unavailable, using it raises an exception.

Some more reading

There is also this University of Chicago article aimed at Python users who are new to R, as well as an excellent article aimed at R users who are new to Python.

Your favorite search engine can tell you more too.

And I’m sure one of you will mention

rpy2, basically reticulate for the Python environment.

Documentation is not as robust as for reticulate, though.

Session info

R

sessionInfo()

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] reticulate_1.38.0

loaded via a namespace (and not attached):
 [1] digest_0.6.36     fastmap_1.2.0     xfun_0.46         Matrix_1.7-0     
 [5] lattice_0.22-6    rappdirs_0.3.3    knitr_1.48        htmltools_0.5.8.1
 [9] png_0.1-8         rmarkdown_2.27    cli_3.6.3         grid_4.4.1       
[13] withr_3.0.1       compiler_4.4.1    rstudioapi_0.16.0 tools_4.4.1      
[17] evaluate_0.24.0   Rcpp_1.0.13       yaml_2.3.10       rlang_1.1.4      
[21] jsonlite_1.8.8

Python

py_config()

python:         C:/Users/jjk1938/AppData/Local/miniforge3/envs/iffrug/python.exe
libpython:      C:/Users/jjk1938/AppData/Local/miniforge3/envs/iffrug/python312.dll
pythonhome:     C:/Users/jjk1938/AppData/Local/miniforge3/envs/iffrug
version:        3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:20:11) [MSC v.1938 64 bit (AMD64)]
Architecture:   64bit
numpy:          C:/Users/jjk1938/AppData/Local/miniforge3/envs/iffrug/Lib/site-packages/numpy
numpy_version:  1.26.4
os:             froze

NOTE: Python version was forced by use_python() function

Python

py_list_packages("iffrug")

                    package            version
1                    brotli              1.1.0
2                brotli-bin              1.1.0
3                     bzip2              1.0.8
4           ca-certificates           2024.2.2
5                   certifi           2024.2.2
6             clang_variant                1.0
7                  clangdev              5.0.0
8                 contourpy              1.2.1
9                    cycler             0.12.1
10                   cython             3.0.10
11                fonttools             4.51.0
12                 freetype             2.12.1
13                  gettext             0.22.5
14            gettext-tools             0.22.5
15                     glib             2.80.0
16               glib-tools             2.80.0
17         gst-plugins-base             1.24.1
18                gstreamer             1.24.1
19                      icu               73.2
20             intel-openmp           2024.1.0
21               kiwisolver              1.4.5
22                     krb5             1.21.2
23                    lcms2               2.16
24                     lerc              4.0.0
25              libasprintf             0.22.5
26        libasprintf-devel             0.22.5
27                  libblas              3.9.0
28          libbrotlicommon              1.1.0
29             libbrotlidec              1.1.0
30             libbrotlienc              1.1.0
31                 libcblas              3.9.0
32               libclang13             18.1.3
33               libdeflate               1.20
34                 libexpat              2.6.2
35                   libffi              3.4.2
36                 libflang              5.0.0
37             libgettextpo             0.22.5
38       libgettextpo-devel             0.22.5
39                  libglib             2.80.0
40                 libhwloc             2.10.0
41                 libiconv               1.17
42                  libintl             0.22.5
43            libintl-devel             0.22.5
44            libjpeg-turbo              3.0.0
45                liblapack              3.9.0
46                   libogg              1.3.4
47              libopenblas             0.3.27
48                   libpng             1.6.43
49                libsqlite             3.45.3
50                  libtiff              4.6.0
51                libvorbis              1.3.7
52             libwebp-base              1.4.0
53                   libxcb               1.15
54                  libxml2             2.12.6
55                  libzlib             1.2.13
56                llvm-meta             13.0.1
57                 llvmlite             0.42.0
58    m2w64-gcc-libgfortran              5.3.0
59           m2w64-gcc-libs              5.3.0
60      m2w64-gcc-libs-core              5.3.0
61                m2w64-gmp              6.1.0
62  m2w64-libwinpthread-git 5.0.0.4634.697f757
63               matplotlib              3.8.4
64          matplotlib-base              3.8.4
65                      mkl           2024.1.0
66        msys2-conda-epoch           20160418
67                  munkres              1.1.4
68                    numba             0.59.1
69                    numpy             1.26.4
70                 openjpeg              2.5.2
71                   openmp              5.0.0
72                  openssl              3.2.1
73                packaging               24.0
74                   pandas              2.2.2
75                    pcre2              10.43
76                   pillow             10.3.0
77                      pip               24.0
78                      ply               3.11
79            pthread-stubs                0.4
80           pthreads-win32              2.9.1
81                 pybind11             2.12.0
82          pybind11-global             2.12.0
83                pyparsing              3.1.2
84                     pyqt             5.15.9
85                pyqt5-sip            12.12.2
86                   python             3.12.3
87          python-dateutil              2.9.0
88            python-tzdata             2024.1
89               python_abi               3.12
90                     pytz             2024.1
91                  qt-main             5.15.8
92                    scipy             1.13.0
93               setuptools             69.5.1
94                      sip             6.7.12
95                      six             1.16.0
96                   sparse             0.15.1
97                      tbb          2021.12.0
98                       tk             8.6.13
99                     toml             0.10.2
100                   tomli              2.0.1
101                 tornado                6.4
102                  tzdata              2024a
103                    ucrt       10.0.22621.0
104                      vc               14.3
105            vc14_runtime        14.38.33130
106          vs2015_runtime        14.38.33130
107                   wheel             0.43.0
108             xorg-libxau             1.0.11
109           xorg-libxdmcp              1.1.3
110                      xz              5.2.6
111                    zstd              1.5.5
                                   requirement     channel
1                                 brotli=1.1.0 conda-forge
2                             brotli-bin=1.1.0 conda-forge
3                                  bzip2=1.0.8 conda-forge
4                     ca-certificates=2024.2.2 conda-forge
5                             certifi=2024.2.2 conda-forge
6                            clang_variant=1.0 conda-forge
7                               clangdev=5.0.0 conda-forge
8                              contourpy=1.2.1 conda-forge
9                                cycler=0.12.1 conda-forge
10                               cython=3.0.10 conda-forge
11                            fonttools=4.51.0 conda-forge
12                             freetype=2.12.1 conda-forge
13                              gettext=0.22.5 conda-forge
14                        gettext-tools=0.22.5 conda-forge
15                                 glib=2.80.0 conda-forge
16                           glib-tools=2.80.0 conda-forge
17                     gst-plugins-base=1.24.1 conda-forge
18                            gstreamer=1.24.1 conda-forge
19                                    icu=73.2 conda-forge
20                       intel-openmp=2024.1.0 conda-forge
21                            kiwisolver=1.4.5 conda-forge
22                                 krb5=1.21.2 conda-forge
23                                  lcms2=2.16 conda-forge
24                                  lerc=4.0.0 conda-forge
25                          libasprintf=0.22.5 conda-forge
26                    libasprintf-devel=0.22.5 conda-forge
27                               libblas=3.9.0 conda-forge
28                       libbrotlicommon=1.1.0 conda-forge
29                          libbrotlidec=1.1.0 conda-forge
30                          libbrotlienc=1.1.0 conda-forge
31                              libcblas=3.9.0 conda-forge
32                           libclang13=18.1.3 conda-forge
33                             libdeflate=1.20 conda-forge
34                              libexpat=2.6.2 conda-forge
35                                libffi=3.4.2 conda-forge
36                              libflang=5.0.0 conda-forge
37                         libgettextpo=0.22.5 conda-forge
38                   libgettextpo-devel=0.22.5 conda-forge
39                              libglib=2.80.0 conda-forge
40                             libhwloc=2.10.0 conda-forge
41                               libiconv=1.17 conda-forge
42                              libintl=0.22.5 conda-forge
43                        libintl-devel=0.22.5 conda-forge
44                         libjpeg-turbo=3.0.0 conda-forge
45                             liblapack=3.9.0 conda-forge
46                                libogg=1.3.4 conda-forge
47                          libopenblas=0.3.27 conda-forge
48                               libpng=1.6.43 conda-forge
49                            libsqlite=3.45.3 conda-forge
50                               libtiff=4.6.0 conda-forge
51                             libvorbis=1.3.7 conda-forge
52                          libwebp-base=1.4.0 conda-forge
53                                 libxcb=1.15 conda-forge
54                              libxml2=2.12.6 conda-forge
55                              libzlib=1.2.13 conda-forge
56                            llvm-meta=13.0.1 conda-forge
57                             llvmlite=0.42.0 conda-forge
58                 m2w64-gcc-libgfortran=5.3.0 conda-forge
59                        m2w64-gcc-libs=5.3.0 conda-forge
60                   m2w64-gcc-libs-core=5.3.0 conda-forge
61                             m2w64-gmp=6.1.0 conda-forge
62  m2w64-libwinpthread-git=5.0.0.4634.697f757 conda-forge
63                            matplotlib=3.8.4 conda-forge
64                       matplotlib-base=3.8.4 conda-forge
65                                mkl=2024.1.0 conda-forge
66                  msys2-conda-epoch=20160418 conda-forge
67                               munkres=1.1.4 conda-forge
68                                numba=0.59.1 conda-forge
69                                numpy=1.26.4 conda-forge
70                              openjpeg=2.5.2 conda-forge
71                                openmp=5.0.0 conda-forge
72                               openssl=3.2.1 conda-forge
73                              packaging=24.0 conda-forge
74                                pandas=2.2.2 conda-forge
75                                 pcre2=10.43 conda-forge
76                               pillow=10.3.0 conda-forge
77                                    pip=24.0 conda-forge
78                                    ply=3.11 conda-forge
79                           pthread-stubs=0.4 conda-forge
80                        pthreads-win32=2.9.1 conda-forge
81                             pybind11=2.12.0 conda-forge
82                      pybind11-global=2.12.0 conda-forge
83                             pyparsing=3.1.2 conda-forge
84                                 pyqt=5.15.9 conda-forge
85                           pyqt5-sip=12.12.2 conda-forge
86                               python=3.12.3 conda-forge
87                       python-dateutil=2.9.0 conda-forge
88                        python-tzdata=2024.1 conda-forge
89                             python_abi=3.12 conda-forge
90                                 pytz=2024.1 conda-forge
91                              qt-main=5.15.8 conda-forge
92                                scipy=1.13.0 conda-forge
93                           setuptools=69.5.1 conda-forge
94                                  sip=6.7.12 conda-forge
95                                  six=1.16.0 conda-forge
96                               sparse=0.15.1 conda-forge
97                               tbb=2021.12.0 conda-forge
98                                   tk=8.6.13 conda-forge
99                                 toml=0.10.2 conda-forge
100                                tomli=2.0.1 conda-forge
101                                tornado=6.4 conda-forge
102                               tzdata=2024a conda-forge
103                          ucrt=10.0.22621.0 conda-forge
104                                    vc=14.3 conda-forge
105                   vc14_runtime=14.38.33130 conda-forge
106                 vs2015_runtime=14.38.33130 conda-forge
107                               wheel=0.43.0 conda-forge
108                         xorg-libxau=1.0.11 conda-forge
109                        xorg-libxdmcp=1.1.3 conda-forge
110                                   xz=5.2.6 conda-forge
111                                 zstd=1.5.5 conda-forge

Footnotes

https://lilicoding.github.io/papers/wang2020assessing.pdf
https://rstudio.github.io/reticulate/articles/python_packages.html
https://mamba.readthedocs.io/en/latest/index.html
https://rstudio.github.io/reticulate/
https://rstudio.github.io/reticulate/articles/calling_python.html
https://rstudio.github.io/reticulate/articles/rstudio_ide.html
https://rstudio.github.io/reticulate/articles/r_markdown.html
https://quarto.org/docs/computations/python.html
https://rstudio.github.io/reticulate/articles/calling_python.html#type-conversions
https://rstudio.github.io/reticulate/articles/arrays.html
https://rstudio.github.io/reticulate/articles/calling_python.html#sparse-matrices

Agenda

Agenda

About Me

Who is Jennifer Knack?

Quick aside: RStudio

Disclaimer

Don’t use Jupyter Notebooks.

RStudio is objectively the best IDE

“But why not just use VSCode?”

Setup

Python install and package management

Setting up RStudio to run Python

Setting up RStudio to run Python

Setting up RStudio to run Python

reticulate

What does reticulate do?5

A note from Posit about the philosophy behind Python tools in Rstudio

What does reticulate do?

Set your environment

What does reticulate do?

The py object

The r object

Importing modules

Sourcing scripts

Executing code

Working in RMarkdown7

Working in Quarto

Working with Python interactively

What does reticulate do?

Type conversions

Conversion table

Controling when conversion happens

Defining the conversion

Indices

Arrays

The TL;DR10

More array considerations

What about arrays from R to Python?

Reshaping arrays

Sparse matrices

Data Frames

The important points

The important points

Using Pandas nullable data types

Final Points

For advanced Python users

Access to Python help from R

Some more reading

And I’m sure one of you will mention

Session info

R

Python

Python

Footnotes

What does reticulate do?⁵

The `py` object

The `r` object

Working in RMarkdown⁷

The TL;DR¹⁰